Image domain based noise reduction for low dose computed tomography fluoroscopy

ABSTRACT

A method of computed-tomography and a computed-tomography apparatus in which x-ray projection data is acquired at a number of views for a scan of an object. Partial images are created from data for a desired number of said views. Full scan images are created from plural ones of the partial images. Non-overlapping time images are created from the full-scan images. Gradient images are also created. An improved image is created by weighting respective ones of the full scan and non-overlapping time images using the gradient image. The improved image has increased sharpness with reduced noise.

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation of and claims the benefit of priorityunder 35 U.S.C. §120 from U.S. Ser. No. 12/539,674, filed Aug. 12, 2009,the contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to reducing noise in computed tomography(CT) images during CT-fluoroscopy.

2. Discussion of the Background

CT-fluoroscopy involves continuous scanning of a slice or volume of asubject for monitoring in real time, such as monitoring interventions.If a regular dose of x-rays is used, the subject will be exposed to alarge x-ray dose. If a lower dose is used, then image noise isincreased. In CT, image noise is inversely proportional to the squareroot of the x-ray tube current. As the tube current is decreased toreduce dose, the image noise increases, resulting in poor image quality.One method used to reduce image noise is to average the image slices atthe same location, but this produces blurring of the edges since thereis bound to be movement of the subject, voluntary or involuntary, duringthe scan. For example, involuntary motion can be due to breathing orbeating of the heart.

SUMMARY OF THE INVENTION

One aspect of the present invention is a computed-tomography methodincluding exposing an object with x-rays at a plurality of scans at aposition of the object to obtain projection data at a plurality ofviews, defining a group of views, where each scan includes a firstnumber of the groups, generating first images respectively usingprojection data from each group of views, generating second images fromplural ones of the first images, generating third images by averagingrespective pluralities of the second images, generating a gradient imageusing at least one of the second and third images, and generating adisplay image by weighting one the of second images and one of the thirdimages using the gradient image.

In another aspect of the invention, a computed-tomography apparatusincludes an x-ray source to expose an object with x-rays at a pluralityof scans at a position of the object to obtain projection data at aplurality of views, an x-ray detector, a data collection unit, a dataprocessing unit connected to the data collection unit, and a display.The data processing unit includes a memory storing x-ray projection datafor a plurality of scans at a position of an object to obtain projectiondata at a plurality of views, and the data processing unit generatesfirst images respectively using projection data from each group ofviews, generates second images from plural ones of the first images,generates third images by averaging respective pluralities of the secondimages, generates a gradient image using at least one of the second andthird images, and generates a display image on the display by weightingone the of second images and one of the third images using the gradientimage.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the invention and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a diagram of a system according to the invention;

FIG. 1A is a diagram of the processing unit of FIG. 1;

FIG. 2 is a matrix of views collected over one rotation of the x-raysource;

FIG. 3 is a diagram of view blocks and image reconstruction over theview blocks;

FIG. 4 is a diagram illustrating partial images;

FIG. 5 is a diagram illustrating full-scan images;

FIG. 6 is a diagram illustrating non-overlapping time images;

FIG. 7 is a diagram illustrating combining images;

FIG. 8 is a graph illustrating a blending curve;

FIG. 9 is a graph illustrating gradient values in an image;

FIG. 10 is a graph of blending factor as a function of gradient value;and

FIG. 11 shows full-scan, non-overlapping and blended images.

DETAILED DESCRIPTION

FIG. 1 shows an x-ray computed tomographic imaging device according tothe present invention. The device may be operated as different x-raydoses to carry out different types of scanning, such as CT fluoroscopy.The projection data measurement system constituted by gantry 1accommodates an x-ray source 3 that generates a cone-beam of x-ray fluxapproximately cone-shaped, and a two-dimensional array type x-raydetector 5 consisting of a plurality of detector elements arranged intwo-dimensional fashion, i.e., a plurality of elements arranged in onedimension stacked in a plurality of rows. X-ray source 3 andtwo-dimensional array type x-ray detector 5 are installed on a rotatingring 2 in facing opposite sides of a subject, who is laid on a slidingsheet of a bed 6. Two-dimensional array type x-ray detector 5 is mountedon rotating ring 2. Each detector element will correspond with onechannel. X-rays from x-ray source 3 are directed on to subject throughan x-ray filter 4. X-rays that have passed through the subject aredetected as an electrical signal by two-dimensional array type x-raydetector 5.

X-ray controller 8 supplies a trigger signal to high voltage generator7. High voltage generator 7 applies high voltage to x-ray source 3 withthe timing with which the trigger signal is received. This causes x-raysto be emitted from x-ray source 3. Gantry/bed controller 9 synchronouslycontrols the revolution of rotating ring 2 of gantry 1 and the slidingof the sliding sheet of bed 6. System controller 10 constitutes thecontrol center of the entire system and controls x-ray controller 8 andgantry/bed controller 9 such that, as seen from the subject, x-raysource 3 executes so-called helical scanning, in which it moves along ahelical path. Specifically, rotating ring 2 is continuously rotated withfixed angular speed while the sliding plate is displaced with fixedspeed, and x-rays are emitted continuously or intermittently at fixedangular intervals from x-ray source 3. The source may also be scannedcircularly.

The output signal of two-dimensional array type x-ray detector 5 isamplified by a data collection unit 11 for each channel and converted toa digital signal, to produce projection data. The projection data outputfrom data collection unit 11 is fed to processing unit 12. Processingunit 12 performs various processing using the projection data. Unit 12performs interpolation, backprojection and reconstruction. Unit 12determines backprojection data reflecting the x-ray absorption in eachvoxel. In the helical scanning system using a cone-beam of x-rays, theimaging region (effective field of view) is of cylindrical shape withradius o) (is there a word missing here?) centered on the axis ofevolution. Unit 12 defines a plurality of voxels (three-dimensionalpixels) in this imaging region, and finds the backprojection data foreach voxel. The three-dimensional image data or tomographic image datacompiled by using this backprojection data is sent to display device 14,where it is displayed visually as a three-dimensional image ortomographic image.

In typical CT operation, projection data is collected over one rotationof the x-ray source (full scan). The number of views collected perrotation in time (T_(Rot)) is N_(VPR), and during each view, data iscollected from a set of detectors N_(d). There may be one or more rowsof detectors. For ease of explanation, a detector with one row isconsidered. The views collected over one rotation can be represented asa matrix shown in FIG. 2. Each cell in the matrix represents a sample ofthe data collected at any given view (y-axis) and any given channel(x-axis).

A more detailed view of collection unit 11 and processing unit 12 isshown in FIG. 1A. The projection data is collected and the data for eachof a desired number of views is stored in a register or portion ofmemory unit 11-1 to 11-n. FIG. 1A will be described in more detailbelow.

For CT fluoroscopy, the same slice position is scanned repeatedly formore than one rotation (N_(Rot)). The total number of views collected isgiven by N_(Rot)|N_(VPR) compared with just N_(VPR) in the case oftypical CT operation. Since there is a continuous feed of the views, itis not necessary to wait until the end of an integral number of T_(Rot)to reconstruct an image. A real time image may be reconstructed usingdata views equal to N_(VPR) at any given time (the views are countedbackwards from any point in time). Preferably, real-time images arereconstructed at a desired fraction of the rotation, such as every ¼ or⅙ rotation.

FIG. 3 illustrates an example with the number of sections per rotationN_(SPR)=4. An image may be reconstructed every T_(Rot)/N_(SPR). As anexample, for a rotation time T_(Rot)=1 sec, an image may be producedevery 0.25 sec. This provides an effect similar to real-time imageproduction or CT fluoroscopy. In FIG. 3, while N_(SPR)=4, it can take onother values such as 6 or 8. The higher the number the more the imageappears to be real-time.

The upper limit is determined by hardware speed and memory needed toreconstruct images. For example, having four partial images per secondimplies four displayed images per second. In an extreme limit, in amathematical sense, a partial image after every view may be created,that is 900 partial images per second or 900 displayed images per second(in this example). However, for the human eye, anything beyond 25-30images per second is not significant. Hence, in practice no more thanabout 20 or 25 partial images per second (900 views) may be computed toprovide good quality partial images. Note that for example purposes, 900views per second are used but this number can take on other values asneeded.

As an example, assume that a total of 1800 views are collected and 900views are required to reconstruct 1 image (Full-Scan). Then, in theoryan image can be reconstructed using the view ranges (1 . . . 900), (2 .. . 901), (3 . . . 902) & so on. However, in practice, the ability ofthe hardware to keep up with the pace of reconstruction may be limited.

In another example, if N_(VPR)=900, each view block contains 225 (900/4)views. There will be a significant overlap in terms of views whenreconstructing consecutive images. It is therefore not necessary tobackproject N_(VPR) views to reconstruction every single image. Partialimages, shown in FIG. 4, may be used. Each partial image PI is formed bybackprojecting only those views within the block. For example, PI(0) isa partial image formed from view block n=0, etc. Full scan images (FS)are formed by:

${{F\; {S(k)}} = {\sum\limits_{i = 0}^{N_{SPR} - 1}{P\; {I( {k - i} )}}}},{{{where}\mspace{14mu} N_{SPR}} > 1},{k \geq {N_{SPR} - 1}}$

In the example of FIG. 4,

First image=PI(0)+PI(1)+PI(2)+PI(3)=FS(3)

Second image=first image−PI(0)+PI(4)=FS(4)

Third image=second image−PI(1)+PI(5)=FS(5)

Using one adding and one subtracting operation to create the imagesreduces the number of operations as opposed to three additions. Here, apartial image (PI) can be computed from as small as one view. In theexample, 900 consecutive (in time) partial images may be added added togive one full scan image. Computationally, using larger number of views(such as 225 in the example) to create partial images is more practical.Further, partial images may be computed using a partial scan, such as ahalf-scan image.

According to the invention, the images may be averaged before beingdisplayed. This is illustrated in FIG. 5. In FIG. 5, OTA denotesOverlapping Time Average. The displayed images OTA are computed in unit12 by:

${{O\; T\; {A(k)}} = {\frac{1}{N_{OTA}}{\sum\limits_{i = 0}^{N_{OTA} - 1}{F\; {S( {k - i} )}}}}},{{{where}\mspace{14mu} \{ {N_{OTA},N_{SPR}} \}} > {1\mspace{14mu} {and}\mspace{14mu} k} \geq {N_{SPR} - 1 + N_{OTA}}}$

In this example:

First Display Image OTA(5)=average (FS(3)+FS(4)+FS(5))

Second Display Image OTA(6)=average (FS(4)+FS(5)+FS(6))

The above OTA approach works ideally when the object being scanned isstationary. However, when there is voluntary or involuntary motion,edges in the displayed image may be blurred. In a second approach tonoise reduction, non-overlapping time images (NTA) are averaged. Theseimages are smooth (less noise). This is illustrated in FIG. 6. The NTAimages are computed by:

${{N\; T\; {A(k)}} = {\frac{1}{N_{NTA}}{\sum\limits_{i = 1}^{N_{NTA}}{F\; {S( {{i \cdot N_{SPR}} - 1} )}}}}},{{{where}\mspace{14mu} N_{NTA}} > {1\mspace{14mu} {and}\mspace{14mu} k} \geq {{N_{NTA} \cdot N_{SPR}} - 1}}$

N_(NTA) is defined as the number of non-overlapping time average images.For example, NTA(11)=FS(3)+FS(7)+FS(11).

FIG. 7 illustrates a further approach to producing an improved imaged.At the end of any view block, there are two different images that may bedisplayed, the NTA and FS images. The NTA image (smooth image) iscombined with the FS image (sharp image) to produce an image with sharpedges without degrading the image smoothness. Here, smoothedimage(11)=FS(11)++NTA(11). The symbol “++” is used to denote a blend ofthe images, and not an addition of corresponding voxels in the 2 images.The FS or NTA image may be defined by the newest collected view blockwhich is 11 in the schematic of FIG. 6. Since this is ‘real-time’ theviews in view block 12 are not being used for computation as yet,although they might be getting collected as the hardware computesFS(11), NTA(11) and FS(11)++NTA(11).

A gradient image, described in more detail below, is used to determinethe contribution to each pixel in the display image from the NTA imageand from the FS image. For pixels in the gradient image that have a highvalue (indicating an edge), the pixels in the display image will have asignificantly larger contribution from the FS image (sharp image) andpixels in the display image that have a low value (indicating smoothregions) will have a larger contribution from the NTA image (smoothimage).

The gradient image may be obtained as follows:

In a first approach, a difference of consecutive FS images is found, andthere is (N_(SPR)−1)/N_(SPR) rotation overlap.

Grad1_(k) =abs(FS(k)−FS(k−1)), where k≧N _(SPR).

In a second approach, a difference of FS images is found, with nooverlap between the images.

Grad2_(m) =abs(FS(m)−FS(m−N _(SPR))), where m≧2·N _(SPR)−1

In a third approach, a difference between FS and NTA images is found

Grad3_(p) =abs(FS−NTA _(p)), where p≧N _(NTA) ·N _(SPR)−1

If there is object motion (as is usually the case), scheme 1 is a betterapproach than scheme 2.

Once the gradient image is obtained, the gradient, FS and NTA images areblended. FIG. 8 represents one blending curve, which is represented bythe following equation:

$\frac{1}{1 + ^{{- {({x - x_{0}})}}/w}}$

Here, x₀ and w are parameters, where x₀ represents the “center” of thecurve and w controls the “width” of the curve.

The parameters may be chosen by an operator or can be set automaticallydepending on the scan conditions and the slice position in the objectbeing imaged. FIG. 8 shows typical values of x₀=40 and w=15. In FIG. 8,x₀ and w were selected and plugged into the above equation to obtain thecurve. These values are just an example. In general, x_(o) may beautomatically selected by computing the average value of voxels in thegradient image, and w is set based on image quality. As shown in FIGS. 8and 10, w can take on a range of values, such as between 15 and 30.

The gradient curve remains fixed for each pixel. In other words, the‘shape’ of the curve does NOT depend on ‘x’ value, which would thegradient value at any voxel. Therefore, going from one voxel to anotheris tantamount to moving along the x-axis which would in turn yield acorresponding value (alpha) on the y-axis. However, the value of α foreach pixel is different and this value is determined by the value of thegradient at that pixel, and is given by:

$\frac{1}{1 + ^{{- {({x - x_{0}})}}/w}}$

For each pixel in the gradient image, a new value of α is determinedbased on the gradient value.

At any given pixel, if the gradient value is high, a higher value of αis used such that a higher contribution to the displayed image comesfrom the FS (sharp) image and, on the other hand, if the gradient valueis low, this means that the pixel belongs to a low frequency region anda higher contribution to the displayed image comes from the NTA (smooth)image. The following equation describes the blending to obtain theblended image BI.

Bl _(p)(n)=(1−a)NTA _(p)(n)+α·FS _(p)(n),

where 0<p<Number of pixels.

The gradient curve may be automatically selected. When the gradientimage is computed, the statistics (mean, median and standard deviation)of the noise values in a soft-tissue region may be computed. On thex-axis, which represents the gradient value, the soft-tissue region andthe high gradient regions will be segregated as shown in FIG. 9. A pointon the gradient axis (x-axis) which results in weight=0.75 is termed asthe pivot point. This pivot point is the value of ‘w’ (gradient value)that gives a fixed value of blending weight=0.75. Thus, using thelocation of pivot point with respect to the soft tissue regionstatistics, the blending curve can be automatically chosen. FIG. 10illustrates different curves, for w=1, 5, 10 and 20. Curves for othervalues of w may be generated and used, as needed.

An example of an image obtained according to the invention isillustrated in FIG. 11. The top image is the FS image, the middle imageis the NTA image and the bottom image is the blended image obtained fromthe above equation. Three regions are indicated in the image. Region 91shows the sharp tip of a needle. The same needle in the NTA image isblurred. Region 92 shows that the edges in the blended image are muchsharper than the NTA image. Region 93 shows how noise is reduced in theblended image compared to the FS image. Thus, according to theinvention, noise can be reduced while maintaining sharpness.

A more detailed view of processing unit 12 is shown in FIG. 1A. Theprojection data is collected and the data for each of desired number ofviews is processed in processing unit 12 by processor 16 to create thepartial images PI(n) and stored them in registers or memory portions15-1 to 15-n of memory 15. Processing unit 12 also generates the FS(n)images, OTA images, NTA image, and gradient images and stores them inother registers or portions 15-o, 15-p, . . . of memory 15. Processingunit performs the blending using blending curves stored in register ormemory portion 15-m, and selects the blending curve, as described above,to create the weighted images. The weighted images are also stored in15-0, 15-p, . . . as needed. The images generated in processing unit 12are sent to display 14 for display to the user. The images created andstored may also be transferred to other users or systems using a networksuch as a LAN, wireless LAN or the internet connected to the CTapparatus.

The invention may also be embodied in the form a computer-readablemedium containing a stored program to cause a computer to carry out thevarious operations and functions described above.

Numerous other modifications and variations of the present invention arepossible in light of the above teachings. It is therefore to beunderstood that within the scope of the appended claims, the inventionmay be practiced otherwise than as specifically described herein.

1. A method for generating computed-tomography image, comprising:exposing an object with x-rays at a plurality of scans at a position ofsaid object to obtain projection data at a plurality of views;generating first images serially in time using the projection data;generating second images by smoothing respective pluralities of saidfirst images; generating a gradient image based on at least one of saidfirst images; and generating a display image by weight blending one saidof first images and one of said second images using said gradient image.2. A method as recited in claim 1, comprising: defining a plurality ofsaid views as a view block; generating consecutive third images fromrespective consecutive view blocks; and generating each of said firstimages from a plurality of consecutive third images.
 3. A method asrecited in claim 2, comprising: using projection data from said viewblocks to produce a respective plurality of said third images.
 4. Amethod as recited in claim 2, comprising: generating a first one of saidfirst images using a first plurality of said third images; andgenerating a second one of said first images by subtracting a first oneof said third images from said first one of said first images and addinga next third image subsequent to said plurality of third images to saidfirst one of said first images.
 5. A method as recited in claim 1,wherein said second images comprise non-overlapping time images(NTA(k)), said first images are given by FS(k), and generating saidsecond images comprises:${{N\; T\; {A(k)}} = {\frac{1}{N_{NTA}}{\sum\limits_{i = 1}^{N_{NTA}}{F\; {S( {{i \cdot N_{SPR}} - 1} )}}}}},{{{where}\mspace{14mu} N_{NTA}} > {1\mspace{14mu} {and}\mspace{14mu} k} \geq {{N_{NTA} \cdot N_{SPR}} - 1}}$where: N_(NTA) is a number of NTA images, and N_(SPR) is a number ofsections per rotation of said x-ray source.
 6. A method as recited inclaim 1, wherein first images are given by FS(k) and said gradientimages are given by:Grad_(k) =abs(FS(k)−FS(k−1)), where k≧N _(SPR). where N_(SPR) is anumber of sections per rotation of said x-ray source.
 7. A method asrecited in claim 1, wherein said first images are given by FS(m) andsaid gradient images are given by:Grad_(m) =abs(FS(m)−FS(m−N _(SPR))), where m≧2·N _(SPR)−1 where N_(SPR)is a number of sections per rotation of said x-ray source.
 8. A methodas recited in claim 1, wherein said first images are given by FS(k),said third images are given by NTA_(p) and said gradient images aregiven by:Grad_(p) =abs(FS−NTA _(p)), where p≧N _(NTA) ·N _(SPR)−1 where: N_(NTA)is a number of said second images N_(SPR) is a number of sections perrotation of said x-ray source.
 9. A method as recited in claim 1,wherein said first images are FS_(p)(n), said second images areNTA_(p)(n) and said display images are given as:Bl _(p)(n)=(1−α)NTA _(p)(n)+α·FS _(p)(n) where α is a weighting factor.10. A method as recited in claim 9, wherein α is given as:$\frac{1}{1 + ^{{- {({x - x_{0}})}}/w}}.$
 11. A method as recited inclaim 9, comprising: using a blending curve to weight said first andsecond images.
 12. A method as recited in claim 11, comprising:automatically selecting said blending curve.
 13. A computed-tomographyapparatus, comprising: an x-ray source to expose an object with x-raysat a plurality of scans at a position of said object to obtainprojection data at a plurality of views; an x-ray detector; a datacollection unit; a data processing unit connected to said datacollection unit; and a display, wherein: said data processing unitincludes a memory storing x-ray projection data for a plurality of scansat a position of the object to obtain projection data at a plurality ofviews; and said data processing unit generates first images serially intime using the projection data, generates second images by smoothingrespective pluralities of said first images, generates a gradient imagebased on at least one of said first images, and generates a displayimage by weight blending one said of first images and one of said secondimages using said gradient image.
 14. An apparatus as recited in claim13, wherein said second images comprise non-overlapping time images(NTA(k)), said first images are given by FS(k), and said second imagesare generated by said data processing unit as:${{N\; T\; {A(k)}} = {\frac{1}{N_{NTA}}{\sum\limits_{i = 1}^{N_{NTA}}{F\; {S( {{i \cdot N_{SPR}} - 1} )}}}}},{{{where}\mspace{14mu} N_{NTA}} > {1\mspace{14mu} {and}\mspace{14mu} k} \geq {{N_{NTA} \cdot N_{SPR}} - 1}}$where: N_(NTA) is a number of NTA images, and N_(SPR) is a number ofsections per rotation of said x-ray source.
 15. An apparatus as recitedin claim 13, wherein first images are given by FS(k) and said gradientimages are given by:Grad_(k) =abs(FS(k)−FS(k−1)), where k≧N _(SPR). where N_(SPR) is anumber of sections per rotation of said x-ray source.
 16. An apparatusas recited in claim 13, wherein said first images are given by FS(m) andsaid gradient images are given by:Grad_(m) =abs(FS(m)−FS(m−N _(SPR))), where m≧2·N _(SPR)−1 where N_(SPR)is a number of sections per rotation of said x-ray source.
 17. Anapparatus as recited in claim 13, wherein said first images are given byFS(k), said second images are given by NTA_(p) and said gradient imagesare given by:Grad_(p) =abs(FS−NTA _(p)), where p≧N _(NTA) ·N _(SPR)−1 where: N_(NTA)is a number of said second images N_(SPR) is a number of sections perrotation of said x-ray source.
 18. An apparatus as recited in claim 13,wherein said first images are FS_(p)(n), said second images areNTA_(p)(n) and said display images are given as:Bl _(p)(n)=(1−α)NTA _(p)(n)+α·FS _(p)(n) where α is a weighting factor.19. An apparatus as recited in claim 18, wherein α is given as:$\frac{1}{1 + ^{{- {({x - x_{0}})}}/w}}.$
 20. An apparatus as recitedin claim 18, comprising: said data collection unit using a blendingcurve to weight said first and second images.
 21. An apparatus asrecited in claim 20, comprising said data collection unit automaticallyselecting said blending curve.